



'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase:   Optional[int]					=		logging.get_logger(__name__)

lowerCAmelCase:   Optional[int]					=		{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
lowerCAmelCase:   List[str]					=		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def  lowerCamelCase__			(		_A					,     _A					,     _A					,     _A					,     _A							):
  for attribute in key.split('.'							):
    a		:   Union[str, Any]	  =	getattr(_A					,     _A							)

  if weight_type is not None:
    a		:   str	  =	getattr(_A					,     _A							).shape
  else:
    a		:   Any	  =	hf_pointer.shape

  assert hf_shape == value.shape, (
      f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
      f""" {value.shape} for {full_name}"""
  )

  if weight_type == "weight":
    a		:   Dict	  =	value
  elif weight_type == "weight_g":
    a		:   Optional[Any]	  =	value
  elif weight_type == "weight_v":
    a		:   Dict	  =	value
  elif weight_type == "bias":
    a		:   Any	  =	value
  else:
    a		:   List[Any]	  =	value

  logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""							)
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Any	  =	[]
  a		:   str	  =	fairseq_model.state_dict()

  a		:   int	  =	hf_model.feature_extractor

  # if encoder has different dim to decoder -> use proj_weight
  a		:   str	  =	None

  for name, value in fairseq_dict.items():
    a		:   List[Any]	  =	False
    if "conv_layers" in name:
      load_conv_layer(
          _A					,     _A					,     _A					,     _A					,     hf_model.config.feat_extract_norm == 'group'					,     )
      a		:   Optional[Any]	  =	True
    elif name.split('.'							)[0] == "proj":
      a		:   Tuple	  =	fairseq_model.proj
      a		:   int	  =	True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
          a		:   List[Any]	  =	True
          if "*" in mapped_key:
            a		:   Union[str, Any]	  =	name.split(_A							)[0].split('.'							)[-2]
            a		:   Optional[Any]	  =	mapped_key.replace('*'					,     _A							)
          if "weight_g" in name:
            a		:   Optional[int]	  =	'weight_g'
          elif "weight_v" in name:
            a		:   Dict	  =	'weight_v'
          elif "bias" in name:
            a		:   Union[str, Any]	  =	'bias'
          elif "weight" in name:
            a		:   Dict	  =	'weight'
          else:
            a		:   Optional[Any]	  =	None
          set_recursively(_A					,     _A					,     _A					,     _A					,     _A							)
        continue
    if not is_used:
      unused_weights.append(_A							)

  logger.warning(f"""Unused weights: {unused_weights}"""							)

  return proj_weight
def  lowerCamelCase__			(		_A					,     _A					,     _A					,     _A					,     _A							):
  a		:   Tuple	  =	full_name.split('conv_layers.'							)[-1]
  a		:   str	  =	name.split('.'							)
  a		:   Dict	  =	int(items[0]							)
  a		:   List[Any]	  =	int(items[1]							)

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
      )
      a		:   Dict	  =	value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
      )
      a		:   Optional[int]	  =	value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
          " found."
      )
      a		:   List[Any]	  =	value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
      )
      a		:   List[Any]	  =	value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
  else:
    unused_weights.append(_A							)
def  lowerCamelCase__			(		_A							):
  a  ,      a		:   Union[str, Any]	  =	emb.weight.shape
  a		:   str	  =	nn.Linear(_A					,     _A					,     bias=_A							)
  a		:   Tuple	  =	emb.weight.data
  return lin_layer
def  lowerCamelCase__			(		_A							):
  with open(_A					,     'r'					,     encoding='utf-8'							) as f:
    a		:   int	  =	f.readlines()
    a		:   Optional[Any]	  =	[line.split(' '							)[0] for line in lines]

  a		:   List[str]	  =	len(_A							)

  a		:   Any	  =	{
      '<s>': 0,
      '<pad>': 1,
      '</s>': 2,
      '<unk>': 3,
  }

  vocab_dict.update(dict(zip(_A					,     range(4					,     num_words + 4							)							)							)							)
  return vocab_dict
@torch.no_grad()
def  lowerCamelCase__			(		_A					,     _A					,     _A					,     _A					,     _A					,     _A					,     _A					,     ):
  a		:   Optional[int]	  =	WavaVecaConfig.from_pretrained(_A							)
  a		:   str	  =	SpeechaTextaConfig.from_pretrained(
      _A					,     vocab_size=_A					,     decoder_layers=_A					,     do_stable_layer_norm=_A							)

  a		:   Optional[int]	  =	WavaVecaFeatureExtractor(
      feature_size=1					,     sampling_rate=1_6000					,     padding_value=0					,     do_normalize=_A					,     return_attention_mask=_A					,     )

  a  ,      a  ,      a		:   Tuple	  =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]					,     arg_overrides={'data': '/'.join(dict_path.split('/'							)[:-1]							)}							)
  a		:   str	  =	model[0].eval()

  # set weights for wav2vec2 encoder
  a		:   Optional[int]	  =	WavaVecaModel(_A							)
  a		:   List[str]	  =	recursively_load_weights_wavaveca(model.encoder					,     _A							)

  a		:   Any	  =	SpeechaTextaForCausalLM(_A							)
  a  ,      a		:   List[str]	  =	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					,     strict=_A							)

  # set output linear layer
  unexpected_keys.remove('embed_out'							)
  a		:   List[str]	  =	nn.Parameter(model.decoder.embed_out.detach()							)

  # layer norm is init to identity matrix so leaving it is fine
  logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""							)
  logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""							)

  a		:   Tuple	  =	SpeechEncoderDecoderModel(encoder=_A					,     decoder=_A							)
  a		:   List[Any]	  =	False

  # add projection layer
  a		:   str	  =	nn.Parameter(projection_layer.weight							)
  a		:   Optional[int]	  =	nn.Parameter(projection_layer.bias							)

  a		:   Tuple	  =	create_vocab_dict(_A							)

  with open(os.path.join(_A					,     'vocab.json'							)					,     'w'							) as fp:
    json.dump(_A					,     _A							)

  a		:   int	  =	SpeechaTextaTokenizer(os.path.join(_A					,     'vocab.json'							)							)
  tokenizer.save_pretrained(_A							)

  a		:   Dict	  =	hf_wavavec.config.to_dict()
  a		:   Any	  =	tokenizer.pad_token_id
  a		:   int	  =	tokenizer.bos_token_id
  a		:   Optional[int]	  =	tokenizer.eos_token_id
  a		:   Tuple	  =	'speech_to_text_2'
  a		:   Any	  =	'wav2vec2'

  a		:   Union[str, Any]	  =	SpeechEncoderDecoderConfig.from_dict(_A							)

  hf_wavavec.save_pretrained(_A							)
  feature_extractor.save_pretrained(_A							)


if __name__ == "__main__":
     lowerCAmelCase:   Any					=		argparse.ArgumentParser()
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
     parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
     parser.add_argument(
         '--encoder_config_path',
         default='facebook/wav2vec2-large-lv60',
         type=str,
         help='Path to hf encoder wav2vec2 checkpoint config',
     )
     parser.add_argument(
         '--decoder_config_path',
         default='facebook/s2t-small-mustc-en-fr-st',
         type=str,
         help='Path to hf decoder s2t checkpoint config',
     )
     parser.add_argument('--vocab_size', default=1_0_2_2_4, type=int, help='Vocab size of decoder')
     parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

     lowerCAmelCase:   int					=		parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.dict_path,
         encoder_config_path=args.encoder_config_path,
         decoder_config_path=args.decoder_config_path,
         vocab_size=args.vocab_size,
         num_decoder_layers=args.num_decoder_layers,
     )




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   List[str]					=		logging.get_logger(__name__)

lowerCAmelCase:   Union[str, Any]					=		{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """lilt"""







    def __init__( self					:    str		,     __snake_case					:    Dict=3_05_22		,     __snake_case					:    int=7_68		,     __snake_case					:    Union[str, Any]=12		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Dict=30_72		,     __snake_case					:    Tuple="gelu"		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    List[str]=5_12		,     __snake_case					:    List[str]=2		,     __snake_case					:    str=0.02		,     __snake_case					:    Dict=1e-1_2		,     __snake_case					:    int=0		,     __snake_case					:    Any="absolute"		,     __snake_case					:    Tuple=None		,     __snake_case					:    Tuple=4		,     __snake_case					:    List[str]=10_24		,     **__snake_case					:    Union[str, Any]		,     ):
      super().__init__(pad_token_id=__snake_case		,     **__snake_case      )

      a		:   str	  =	vocab_size
      a		:   Optional[Any]	  =	hidden_size
      a		:   Union[str, Any]	  =	num_hidden_layers
      a		:   Tuple	  =	num_attention_heads
      a		:   Optional[Any]	  =	hidden_act
      a		:   Union[str, Any]	  =	intermediate_size
      a		:   Optional[Any]	  =	hidden_dropout_prob
      a		:   Tuple	  =	attention_probs_dropout_prob
      a		:   str	  =	max_position_embeddings
      a		:   Any	  =	type_vocab_size
      a		:   Union[str, Any]	  =	initializer_range
      a		:   int	  =	layer_norm_eps
      a		:   Any	  =	position_embedding_type
      a		:   Any	  =	classifier_dropout
      a		:   str	  =	channel_shrink_ratio
      a		:   Optional[Any]	  =	max_ad_position_embeddings